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Titlebook: Statistical Modelling; Proceedings of the 1 Gilg U. H. Seeber,Brian J. Francis,Gabriele Stecke Conference proceedings 1995 Springer Science

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楼主: Helmet
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IRREML, a tool for fitting a versatile class of mixed models for ordinal data, basic model is the common threshold or grouped continuous model, assuming a underlying continuous variable . which is observed imperfectly. Any family of continuous distributions is a candidate for approximating the distribution of . and a generalised linear mixed model may be specified for its par
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Towards a General Robust Estimation Approach for Generalised Regression Models,model, by combining the optimal B-robust estimation available for M-estimators and an original extension of the bounded-influence methods recently developed for variance components models. Special attention is given to the application of this procedure in the context of panel data models, particular
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NPML estimation of the mixing distribution in general statistical models with unobserved random effmplementation of the methods is straightforward in GLM packages like GLIM4 and S+..An important feature of these computational methods is that they are applicable to a much broader class of models than the GLMs described above, and provide new general computational solutions to fitting a very wide range of models using .
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Statistical Modelling978-1-4612-0789-4Series ISSN 0930-0325 Series E-ISSN 2197-7186
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An Extended Model for Paired Comparisons,The aim of this paper is to present a log-linear formulation of an extended Bradley-Terry model for paired comparisons that allows for simultaneous modelling of ties, order effects, categorical subject-specific covariates as well as object-specific covariates.
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Model Estimation in Nonlinear Regression,Given data from a sample of noisy curves in a nonlinear parametric regression model we consider nonparametric estimation of the model function and the parameters under certain structural assumptions. An algorithm for a consistent estimator is proposed and examples given.
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Subject-Specific and Population-Averaged Questions for Log-Linear Regression Data,This paper illustrates the relation between subject-specific and population-averaged models (.), especially for loglinear regression data with normal random effects. The emphasis is on simple special cases. The practical implications are discussed using an example from the literature.
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